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Salary: USD 175,000 - 275,000 / annual
HealthLeap is an AI operating system that helps hospital care teams identify missed patients who need intervention, improving health outcomes and generating significant cost savings. The company is live at 40+ hospitals with plans to expand to 100+, has grown contracted revenue 13x in the past year, and has raised over $32M. The team is ~25 people, SF-based, and hybrid-friendly.
As Senior Data Engineer, you will build and operate the core data pipelines that move clinical data from hospital systems into model inputs, analytics, and APIs that clinicians use daily. You'll ensure data is usable, reliable, and trustworthy across the entire system.
Key responsibilities:
- Build and operate end-to-end pipelines from hospital data ingestion through transformation and delivery to ML models, analytics, and user-facing APIs
- Produce trusted data for ML pipelines, customer analytics dashboards, and clinical interfaces
- Define data contracts and validation checks that catch missing records, schema changes, and incorrect values
- Handle backfills, late-arriving data, system failures, and recovery procedures
- Collaborate with integration, ML, and product engineers to ensure data reliability across the platform
- Trace problems across systems and own fixes through production
The role requires ownership across the full data stack—ingestion, model inputs, analytics, and product APIs. You'll work in a fast-paced environment where hospital go-lives can require extended hours, though the company prioritizes deep rest and sustainable work.
Interview process: intro call, data pipeline design exercise, practical data problem, and onsite with the team in San Francisco. No LeetCode; you'll use real tools including AI.
REQUIREMENTS:
- 5+ years building production data systems with strong Python and SQL
- Experience owning pipelines that depend on messy, changing external data sources
- Strong data modeling skills and judgment about correctness, monitoring, and recovery
- Ability to trace problems across systems and own fixes through production
STAND-OUT QUALIFICATIONS:
- Experience building data pipelines for ML products
- Experience with clinical data, EHRs, HL7, or FHIR standards
- Early-stage experience building and operating core data systems
